{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shows how to compute covariance using the geostat class\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geostat as geo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in [\"spherical\"]: #, \"gaussian\"]:\n",
    "    \n",
    "    h = np.linspace(0,3,100)\n",
    "    \n",
    "    covar = geo.Covariance(sill=2.0,rang=2,typ=t)\n",
    "    gamma = geo.Variogram(sill=2.0,rang=2,typ=t)\n",
    "    c = covar(h)\n",
    "    g = gamma(h)\n",
    "\n",
    "    plt.plot(h,c,h,g)\n",
    "    plt.grid()\n",
    "    plt.xlabel('h')\n",
    "    plt.ylabel('C(h)')\n",
    "    plt.title(t)\n",
    "    plt.savefig('tmp.png', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.112\n"
     ]
    }
   ],
   "source": [
    "\n",
    "co = geo.Covariance(0.5,3,typ=\"spherical\")\n",
    "\n",
    "print( co(0.1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: type of covariance not yet available:  matern\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h=np.linspace(0,10,100)\n",
    "\n",
    "covar = geo.Covariance(sill=1,rang=1,typ='matern')\n",
    "c = covar(h)\n",
    "\n",
    "plt.plot(h,c)\n",
    "plt.grid()\n",
    "plt.xlabel('h')\n",
    "plt.ylabel('C(h)')\n",
    "plt.title(t)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nu' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-39ff48eca5cd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mco\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgeo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCovariance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtyp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"matern\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnugget\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'nu' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "co = geo.Covariance(nu,range,typ=\"matern\",nugget=0.1)\n",
    "print( co(h) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geostat as geo\n",
    "\n",
    "for t in [\"linear\", \"power\"]:\n",
    "    \n",
    "    h = np.linspace(0,3,300)\n",
    "    gamma = geo.Variogram(sill=2.0,rang=1.9,typ=t,nugget=1)\n",
    "    g = gamma(h)\n",
    "\n",
    "    plt.plot(h,g)\n",
    "    plt.grid()\n",
    "    plt.xlabel('h')\n",
    "    plt.ylabel('C(h)')\n",
    "    plt.title(t)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mattern variogram: range = 1.9, sill =2.0, nugget =0\n",
      "Error:  variogram type:  mattern  not available.\n",
      "        The variogram value is not computed.\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'float' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-ab158583c8fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mgamma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgeo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVariogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msill\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrang\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1.9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtyp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mattern'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgamma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgamma\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\gehrenard\\owncloud\\python\\geostat\\geostat\\base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, h)\u001b[0m\n\u001b[0;32m    134\u001b[0m             \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m         \u001b[0mC\u001b[0m\u001b[1;33m[\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nugget\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "gamma = geo.Variogram(sill=2.0,rang=1.9,typ='mattern')\n",
    "print(gamma)\n",
    "g = gamma(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
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